{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Feature Store Tour - Python API\n",
    " \n",
    "This notebook contains a tour/reference for the Hopsworks feature store Python API on Amazon SageMaker. The notebook is meant to be run on Amazon SageMaker after setting up Hopsworks to work with AWS [(see here)](https://hopsworks.readthedocs.io/en/latest/user_guide/hopsworks/featurestore.html#connecting-from-amazon-sagemaker).\n",
    "\n",
    "The notebook is designed to be used in combination with the Feature Store Tour on Hopsworks, it assumes that you have run the following feature engineering job: [job](https://github.com/logicalclocks/hops-examples/tree/master/featurestore_tour) (**the job is added automatically when you start the feature store tour in Hopsworks. You can run the job by going to the 'Jobs' tab to the left in the Hopsworks project home page**). \n",
    "\n",
    "Which will produce the following model of feature groups in your project's feature store:\n",
    "\n",
    "![Feature Store Model](../images/model.png \"Feature Store Model\")\n",
    "\n",
    "In this notebook we will run queries over this feature store model. We will also create new feature groups and training datasets.\n",
    "\n",
    "We will go from (1) features to (2) training datasets to (3) A trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access the feature store from SageMaker the hopsworks-cloud-sdk needs to be installed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install hopsworks-cloud-sdk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hops import featurestore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This assumes that Hopsworks and AWS were already configured correctly. See [Connecting from Amazon SageMaker](https://hopsworks.readthedocs.io/en/latest/user_guide/hopsworks/featurestore.html#connecting-from-amazon-sagemaker)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.connect('ec2-13-53-124-128.eu-north-1.compute.amazonaws.com', 'demo_featurestore_admin000', hostname_verification=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get The Name of The Project's Feature Store\n",
    "\n",
    "Each project with the feature store service enabled automatically gets its own feature store created. This feature store is only accessible within the project unless you decide to share it with other projects. The name of the feature store is `<project_name>_featurestore`, and you can get the name with the API method `project_featurestore()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'demo_featurestore_admin000_featurestore'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.project_featurestore()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get a List of All Feature Stores Accessible in the Current Project \n",
    "\n",
    "Feature Stores can be shared across projects in a multi-tenant manner, just like any Hopsworks-dataset can. You can read more about sharing datasets at [hops.io](hops.io), but in essence to share a dataset you just have to right click on it in your project. The feature groups in the feature store are located in a dataset called `<project_name>_featurestore.db` in your project.\n",
    "\n",
    "![Share Feature Store](./images/share_featurestore.png \"Share Feature Store\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['demo_featurestore_admin000_featurestore']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_project_featurestores()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying The Feature Store\n",
    "\n",
    "The feature store can be queried programmatically and with raw SQL. When you query the feature store programmatically, the library will infer how to fetch the different features using a **query planner**. \n",
    "\n",
    "![Feature Store Query Planner](../images/query_optimizer.png \"Feature Store Query Planner\")\n",
    "\n",
    "When interacting with the feature store it is sufficient to be familiar with three concepts:\n",
    "\n",
    "- The **feature**, this refer to an individual versioned and documented feature in the feature store, e.g the age of a person.\n",
    "- The **feature group**, this refer to a documented and versioned group of features stored as a Hive table that is linked to a specific Spark/Numpy/Pandas job that takes in raw data and outputs the computed features.\n",
    "- The **training dataset**, this refer to a versioned and managed dataset of features, stored in HopsFS as tfrecords, .csv, .tsv, or parquet.\n",
    "\n",
    "A feature group contains a group of features and a training dataset contains a set of features, potentially from many different feature groups.\n",
    "\n",
    "![Feature Store Concepts](../images/concepts.png \"Feature Store Contents\")\n",
    "\n",
    "When you query the feature store you will always get back the results in a pandas dataframe. This is for scalability reasons. If the dataset is small and you want to work with it in memory you can convert it into a pandas dataframe or a numpy matrix using one line of code as we will demonstrate later on in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch an Individual Feature\n",
    "\n",
    "When retrieving a single feature from the featurestore, the hops-util-py library will infer in which feature group the feature belongs to by querying the metastore, but you can also explicitly specify which featuregroup and version to query. \n",
    "\n",
    "If there are multiple features of the same name in the featurestore, it is required to specify enough information to uniquely identify the feature (e.g specify feature group and version). If no featurestore is provided it will default to the project's featurestore.\n",
    "\n",
    "To read an individual feature, use the method `get_feature(feature_name)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without specifying the feature store, feature group and version, the library will infer it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logical query plan for getting 1 feature from the featurestore created successfully\n",
      "SQL string for the query created successfully\n",
      "Running sql: SELECT team_budget FROM teams_features_1 against the offline feature store\n"
     ]
    },
    {
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       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   team_budget\n",
       "0   12957.0760\n",
       "1    2403.3704\n",
       "2    3390.3755\n",
       "3   13547.4290\n",
       "4    9678.3330"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_feature(\"team_budget\").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also explicitly specify the feature store, feature group, the version, and the return format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logical query plan for getting 1 feature from the featurestore created successfully\n",
      "SQL string for the query created successfully\n",
      "Running sql: SELECT team_budget FROM teams_features_1 against the offline feature store\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12957.0760</td>\n",
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       "      <th>1</th>\n",
       "      <td>2403.3704</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390.3755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9678.3330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team_budget\n",
       "0   12957.0760\n",
       "1    2403.3704\n",
       "2    3390.3755\n",
       "3   13547.4290\n",
       "4    9678.3330"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_feature(\n",
    "    \"team_budget\", \n",
    "    featurestore=featurestore.project_featurestore(), \n",
    "    featuregroup=\"teams_features\", \n",
    "    featuregroup_version = 1\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch an Entire Feature Group\n",
    "\n",
    "You can get an entire featuregroup from the API. If no feature store is provided the API will default to the project's feature store, if no version is provided it will default to version 1 of the feature group. The return format is as a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SQL string for the query created successfully\n",
      "Running sql: SELECT * FROM teams_features_1 against the offline feature store\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9678.3330</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team_budget  team_id  team_position\n",
       "0   12957.0760        1              1\n",
       "1    2403.3704        2              2\n",
       "2    3390.3755        3              3\n",
       "3   13547.4290        4              4\n",
       "4    9678.3330        5              5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_featuregroup(\"teams_features\").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default parameters can be overriden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SQL string for the query created successfully\n",
      "Running sql: SELECT * FROM teams_features_1 against the offline feature store\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12957.0760</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2403.3704</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390.3755</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9678.3330</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team_budget  team_id  team_position\n",
       "0   12957.0760        1              1\n",
       "1    2403.3704        2              2\n",
       "2    3390.3755        3              3\n",
       "3   13547.4290        4              4\n",
       "4    9678.3330        5              5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_featuregroup(\n",
    "    \"teams_features\", \n",
    "    featurestore=featurestore.project_featurestore(), \n",
    "    featuregroup_version = 1\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch A Set of Features\n",
    "\n",
    "When retrieving a list of features from the featurestore, the hops-util-py library will infer which featuregroup the features belongs to by querying the metastore. If the features reside in different featuregroups, the library will also try to infer how to join the features together based on common columns. If the JOIN query cannot be inferred due to existence of multiple features with the same name or non-obvious JOIN query, the user need to supply enough information to the API call to be able to query the featurestore. If the user already knows the JOIN query it can also run featurestore.sql(joinQuery) directly (an example of this is shown further down in this notebook). If no featurestore is provided the API will default to the project's featurestore."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of querying the feature store for a list of features without specifying the feature groups and feature store:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logical query plan for getting 3 features from the featurestore created successfully\n",
      "SQL string for the query created successfully\n",
      "Running sql: SELECT average_player_age, average_attendance, team_budget FROM players_features_1 JOIN attendances_features_1 JOIN teams_features_1 ON players_features_1.`team_id`=attendances_features_1.`team_id` AND players_features_1.`team_id`=teams_features_1.`team_id` against the offline feature store\n"
     ]
    },
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>average_attendance</th>\n",
       "      <th>team_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.65</td>\n",
       "      <td>3271.9340</td>\n",
       "      <td>16758.0660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.67</td>\n",
       "      <td>2695.4463</td>\n",
       "      <td>14580.9480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.67</td>\n",
       "      <td>2701.0522</td>\n",
       "      <td>9290.6380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.88</td>\n",
       "      <td>92301.0860</td>\n",
       "      <td>12957.0760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.18</td>\n",
       "      <td>2823.9960</td>\n",
       "      <td>4134.0903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_player_age  average_attendance  team_budget\n",
       "0               25.65           3271.9340   16758.0660\n",
       "1               25.67           2695.4463   14580.9480\n",
       "2               25.67           2701.0522    9290.6380\n",
       "3               25.88          92301.0860   12957.0760\n",
       "4               26.18           2823.9960    4134.0903"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_features(\n",
    "    [\"team_budget\", \"average_attendance\", \"average_player_age\"]\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also explicitly specify the feature groups where the features reside. Either the feature groups and versions can be specified by prepending feature names with `<feature group name>_<feature group version.`, or by providing a dict with entries of `<feature group name> -> <feature group version>`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logical query plan for getting 3 features from the featurestore created successfully\n",
      "SQL string for the query created successfully\n",
      "Running sql: SELECT attendances_features_1.average_attendance, teams_features_1.team_budget, players_features_1.average_player_age FROM attendances_features_1 JOIN teams_features_1 JOIN players_features_1 ON attendances_features_1.`team_id`=teams_features_1.`team_id` AND attendances_features_1.`team_id`=players_features_1.`team_id` against the offline feature store\n"
     ]
    },
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_attendance  team_budget  average_player_age\n",
       "0           3271.9340   16758.0660               25.65\n",
       "1           2695.4463   14580.9480               25.67\n",
       "2           2701.0522    9290.6380               25.67\n",
       "3          92301.0860   12957.0760               25.88\n",
       "4           2823.9960    4134.0903               26.18"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_features(\n",
    "    [\"teams_features_1.team_budget\", \n",
    "     \"attendances_features_1.average_attendance\", \n",
    "     \"players_features_1.average_player_age\"]\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logical query plan for getting 3 features from the featurestore created successfully\n",
      "SQL string for the query created successfully\n",
      "Running sql: SELECT average_player_age, average_attendance, team_budget FROM attendances_features_1 JOIN players_features_1 JOIN teams_features_1 ON attendances_features_1.`team_id`=players_features_1.`team_id` AND attendances_features_1.`team_id`=teams_features_1.`team_id` against the offline feature store\n"
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       "  </thead>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.18</td>\n",
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       "      <td>4134.0903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_player_age  average_attendance  team_budget\n",
       "0               25.65           3271.9340   16758.0660\n",
       "1               25.67           2695.4463   14580.9480\n",
       "2               25.67           2701.0522    9290.6380\n",
       "3               25.88          92301.0860   12957.0760\n",
       "4               26.18           2823.9960    4134.0903"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_features(\n",
    "    [\"team_budget\", \"average_attendance\", \"average_player_age\"],\n",
    "    featurestore=featurestore.project_featurestore(),\n",
    "    featuregroups_version_dict={\n",
    "        \"teams_features\": 1, \n",
    "        \"attendances_features\": 1,\n",
    "        \"players_features\": 1\n",
    "    }\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have a lot of name collisions and it is not obvious how to infer the JOIN query to get the features from the feature store. You can explicitly specify the argument `join_key` to the API (or you can provide the entire SQL query using the API method `.sql` as we will demonstrate later on in the notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logical query plan for getting 3 features from the featurestore created successfully\n",
      "SQL string for the query created successfully\n",
      "Running sql: SELECT average_player_age, average_attendance, team_budget FROM teams_features_1 JOIN attendances_features_1 JOIN players_features_1 ON teams_features_1.`team_id`=attendances_features_1.`team_id` AND teams_features_1.`team_id`=players_features_1.`team_id` against the offline feature store\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>average_attendance</th>\n",
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       "      <th>2</th>\n",
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       "      <td>9290.6380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.88</td>\n",
       "      <td>92301.0860</td>\n",
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       "      <th>4</th>\n",
       "      <td>26.18</td>\n",
       "      <td>2823.9960</td>\n",
       "      <td>4134.0903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_player_age  average_attendance  team_budget\n",
       "0               25.65           3271.9340   16758.0660\n",
       "1               25.67           2695.4463   14580.9480\n",
       "2               25.67           2701.0522    9290.6380\n",
       "3               25.88          92301.0860   12957.0760\n",
       "4               26.18           2823.9960    4134.0903"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_features(\n",
    "    [\"team_budget\", \"average_attendance\", \"average_player_age\"],\n",
    "    featurestore=featurestore.project_featurestore(),\n",
    "    featuregroups_version_dict={\n",
    "        \"teams_features\": 1, \n",
    "        \"attendances_features\": 1,\n",
    "        \"players_features\": 1\n",
    "    },\n",
    "    join_key = \"team_id\"\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Advanced Eamples of Fetching Sets of Features and Common Pitfalls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting 12 features from 4 different feature groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logical query plan for getting 12 features from the featurestore created successfully\n",
      "SQL string for the query created successfully\n",
      "Running sql: SELECT average_position, sum_player_rating, sum_player_worth, average_attendance, average_player_age, sum_position, team_position, average_player_worth, sum_player_age, sum_attendance, average_player_rating, team_budget FROM season_scores_features_1 JOIN players_features_1 JOIN attendances_features_1 JOIN teams_features_1 ON season_scores_features_1.`team_id`=players_features_1.`team_id` AND season_scores_features_1.`team_id`=attendances_features_1.`team_id` AND season_scores_features_1.`team_id`=teams_features_1.`team_id` against the offline feature store\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_position</th>\n",
       "      <th>sum_player_rating</th>\n",
       "      <th>sum_player_worth</th>\n",
       "      <th>average_attendance</th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>sum_position</th>\n",
       "      <th>team_position</th>\n",
       "      <th>average_player_worth</th>\n",
       "      <th>sum_player_age</th>\n",
       "      <th>sum_attendance</th>\n",
       "      <th>average_player_rating</th>\n",
       "      <th>team_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.15</td>\n",
       "      <td>32269.797</td>\n",
       "      <td>30787.268</td>\n",
       "      <td>3271.9340</td>\n",
       "      <td>25.65</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>26</td>\n",
       "      <td>307.87268</td>\n",
       "      <td>2565.0</td>\n",
       "      <td>65438.680</td>\n",
       "      <td>322.69797</td>\n",
       "      <td>16758.0660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62.45</td>\n",
       "      <td>22957.967</td>\n",
       "      <td>22179.488</td>\n",
       "      <td>2695.4463</td>\n",
       "      <td>25.67</td>\n",
       "      <td>1249.0</td>\n",
       "      <td>36</td>\n",
       "      <td>221.79488</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>53908.926</td>\n",
       "      <td>229.57967</td>\n",
       "      <td>14580.9480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.45</td>\n",
       "      <td>22761.396</td>\n",
       "      <td>18485.990</td>\n",
       "      <td>2701.0522</td>\n",
       "      <td>25.67</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>37</td>\n",
       "      <td>184.85991</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>54021.043</td>\n",
       "      <td>227.61397</td>\n",
       "      <td>9290.6380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.05</td>\n",
       "      <td>719186.300</td>\n",
       "      <td>792043.250</td>\n",
       "      <td>92301.0860</td>\n",
       "      <td>25.88</td>\n",
       "      <td>581.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7920.43260</td>\n",
       "      <td>2588.0</td>\n",
       "      <td>1846021.800</td>\n",
       "      <td>7191.86330</td>\n",
       "      <td>12957.0760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.15</td>\n",
       "      <td>18149.428</td>\n",
       "      <td>17936.293</td>\n",
       "      <td>2823.9960</td>\n",
       "      <td>26.18</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>43</td>\n",
       "      <td>179.36293</td>\n",
       "      <td>2618.0</td>\n",
       "      <td>56479.920</td>\n",
       "      <td>181.49428</td>\n",
       "      <td>4134.0903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   average_position  sum_player_rating  sum_player_worth  average_attendance  \\\n",
       "0             55.15          32269.797         30787.268           3271.9340   \n",
       "1             62.45          22957.967         22179.488           2695.4463   \n",
       "2             58.45          22761.396         18485.990           2701.0522   \n",
       "3             29.05         719186.300        792043.250          92301.0860   \n",
       "4             74.15          18149.428         17936.293           2823.9960   \n",
       "\n",
       "   average_player_age  sum_position  team_position  average_player_worth  \\\n",
       "0               25.65        1103.0             26             307.87268   \n",
       "1               25.67        1249.0             36             221.79488   \n",
       "2               25.67        1169.0             37             184.85991   \n",
       "3               25.88         581.0              1            7920.43260   \n",
       "4               26.18        1483.0             43             179.36293   \n",
       "\n",
       "   sum_player_age  sum_attendance  average_player_rating  team_budget  \n",
       "0          2565.0       65438.680              322.69797   16758.0660  \n",
       "1          2567.0       53908.926              229.57967   14580.9480  \n",
       "2          2567.0       54021.043              227.61397    9290.6380  \n",
       "3          2588.0     1846021.800             7191.86330   12957.0760  \n",
       "4          2618.0       56479.920              181.49428    4134.0903  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_features(\n",
    "    [\"team_budget\", \"average_attendance\", \"average_player_age\",\n",
    "    \"team_position\", \"sum_attendance\", \n",
    "     \"average_player_rating\", \"average_player_worth\", \"sum_player_age\",\n",
    "     \"sum_player_rating\", \"sum_player_worth\", \"sum_position\", \n",
    "     \"average_position\"\n",
    "    ]\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a training dataset from the Feature Store\n",
    "\n",
    "The feature store has an abstraction of a **training dataset**, which is a dataset with a set of features (potentially from many different feature groups) and labels (in case of supervised learning).\n",
    "\n",
    "When you train a machine learning model, you want to use all features that have predictive power and that the model can learn from. At this point, we can create a training dataset of features from several different feature groups and use that for training. That is the purpose of the training dataset abstraction.\n",
    "\n",
    "Of course you can always just save a group of features anywhere inside your project, e.g as a csv, or .tfrecords file. However, by using the feature store you can create managed training datasets. Managed training datasets will show up in the feature registry UI and will automatically be versioned, documented and reproducible.\n",
    "\n",
    "Lets create a dataset called *team_position_prediction* by using the previous set of 12 relevant features from the featurestore. We will combine features from four different feature groups to form this training dataset: \n",
    "\n",
    "- teams_features\n",
    "- attendances_features\n",
    "- players_features\n",
    "- season_scores_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list = [\"team_budget\", \"average_attendance\", \"average_player_age\",\n",
    "    \"team_position\", \"sum_attendance\", \n",
    "     \"average_player_rating\", \"average_player_worth\", \"sum_player_age\",\n",
    "     \"sum_player_rating\", \"sum_player_worth\", \"sum_position\", \n",
    "     \"average_position\"\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a training dataset with the list of features with some extended metadata such as schema (automatically inferred). By default when you create a training dataset it will be in \"tfrecords\" format and statistics will be computed for all features. After the dataset have been created you can view and/or update the metadata about the training dataset from the Hopsworks featurestore UI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First you should check if a training dataset with the same name has been created before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "latest_version = featurestore.get_latest_training_dataset_version(\"team_position_prediction\")\n",
    "print(latest_version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can use the `featurestore.create_training_dataset()` API to create and launch a job which will create your training dataset. You can either pass in a list of feature names from different feature groups to be joined on `join_key` or a pure sql query string for more complex training datasets. The `join_key` is optional and we will infer it from the primary key of the feature groups if it is not provided. The job will be called the same name as your training dataset, in case you want to rerun the creation from the Hopsworks UI. Good practice is to increase the version by 1, but you can also decide to overwrite it with the same version if you set the `overwrite` argument to `True`.\n",
    "\n",
    "By default the dataset is written as \"tfrecords\" to HopsFS but you can specify an alternative `sink` by passing your storage connector name. Please note that the storage connector has to be created in the Hopsworks featurestore UI previously.\n",
    "\n",
    "The `featurestore.create_training_dataset()` API offers additional parameters to modify its behaviour, for the full range of possible arguments please refer to the docs. Most arguments will be familiar to you from the `featurestore.get_features()` API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Dataset job successfully started\n"
     ]
    }
   ],
   "source": [
    "featurestore.create_training_dataset(\n",
    "    training_dataset = \"team_position_prediction\",\n",
    "    features = feature_list,\n",
    "    descriptive_statistics = False,\n",
    "    feature_correlation = False,\n",
    "    feature_histograms = False,\n",
    "    cluster_analysis = False,\n",
    "    training_dataset_version = latest_version + 1,\n",
    "    overwrite=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to utilize the SQL query functionality, you have to know that you can query a feature group by assembling its name with the desired version: **[featuregroup]_[version]**.\n",
    "\n",
    "In the following example we create a training dataset by selecting all features from the 'games_features' feature group with version 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "latest_version = featurestore.get_latest_training_dataset_version(\"games_features_all\")\n",
    "print(latest_version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Dataset job successfully started\n"
     ]
    }
   ],
   "source": [
    "featurestore.create_training_dataset(\n",
    "    training_dataset = \"games_features_all\",\n",
    "    sql_query = \"SELECT * FROM games_features_1\",\n",
    "    descriptive_statistics = False,\n",
    "    feature_correlation = False,\n",
    "    feature_histograms = False,\n",
    "    cluster_analysis = False,\n",
    "    training_dataset_version = latest_version + 1,\n",
    "    overwrite=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Free Text SQL Query from the Feature Store\n",
    "\n",
    "For complex queries that cannot be inferred by the helper functions, enter the sql directly to the method `featurestore.sql()` it will default to the project specific feature store but you can also specify it explicitly. If you are proficient in SQL, this is the most efficient and preferred way to query the feature store."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without specifying the feature store the query will by default be run against the project's feature store:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sql: SELECT * FROM teams_features_1 WHERE team_position < 5 against the offline feature store\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12957.0760</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2403.3704</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390.3755</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team_budget  team_id  team_position\n",
       "0   12957.0760        1              1\n",
       "1    2403.3704        2              2\n",
       "2    3390.3755        3              3\n",
       "3   13547.4290        4              4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.sql(\"SELECT * FROM teams_features_1 WHERE team_position < 5\").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also specify the featurestore to query explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sql: SELECT * FROM teams_features_1 WHERE team_position < 5 against the offline feature store\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12957.0760</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2403.3704</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390.3755</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team_budget  team_id  team_position\n",
       "0   12957.0760        1              1\n",
       "1    2403.3704        2              2\n",
       "2    3390.3755        3              3\n",
       "3   13547.4290        4              4"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.sql(\"SELECT * FROM teams_features_1 WHERE team_position < 5\",\n",
    "                featurestore=featurestore.project_featurestore()).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Featuregroup Visualization\n",
    "\n",
    "As you will see later on in this tutorial, when writing to the featurestore there is an option to also compute statistics of the features. The computed statistics will be stored as attached metadata of featuregroups and visualized in the feature registry. You can also access the statistics from the python API and visualize it in Jupyter notebooks in `%%local`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_featuregroup_distributions(\"players_features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also override default parameters and configure the plotting options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_featuregroup_distributions(\"players_features\", \n",
    "                                                  featurestore=featurestore.project_featurestore(), \n",
    "                                                  featuregroup_version=1, \n",
    "                                                  figsize=(12, 9),\n",
    "                                                  color='lightblue', \n",
    "                                                  log=False, \n",
    "                                                  align=\"center\", \n",
    "                                                  plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_featuregroup_correlations(\"players_features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also override default parameters and configure the plotting options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_featuregroup_correlations(\"players_features\", \n",
    "                                                 featurestore=None, \n",
    "                                                 featuregroup_version=1, \n",
    "                                                 figsize=(8,6),\n",
    "                                                 cmap=\"coolwarm\", \n",
    "                                                 annot=True, \n",
    "                                                 fmt=\".2f\", \n",
    "                                                 linewidths=.05, \n",
    "                                                 plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_featuregroup_clusters(\"players_features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also override default parameters and configure the plotting options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_featuregroup_clusters(\"players_features\", \n",
    "                                featurestore=None, \n",
    "                                featuregroup_version=1, \n",
    "                                figsize=(8,6),\n",
    "                                plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>sum_player_rating</th>\n",
       "      <th>sum_player_worth</th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>team_id</th>\n",
       "      <th>average_player_worth</th>\n",
       "      <th>sum_player_age</th>\n",
       "      <th>average_player_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>count</td>\n",
       "      <td>50.000</td>\n",
       "      <td>50.000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>max</td>\n",
       "      <td>719186.300</td>\n",
       "      <td>792043.250</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>7920.43260</td>\n",
       "      <td>2700.00000</td>\n",
       "      <td>7191.86330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stddev</td>\n",
       "      <td>118708.750</td>\n",
       "      <td>128564.730</td>\n",
       "      <td>0.522913</td>\n",
       "      <td>14.57738</td>\n",
       "      <td>1285.64720</td>\n",
       "      <td>52.29132</td>\n",
       "      <td>1187.08750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mean</td>\n",
       "      <td>71738.375</td>\n",
       "      <td>73451.310</td>\n",
       "      <td>25.568400</td>\n",
       "      <td>25.50000</td>\n",
       "      <td>734.51310</td>\n",
       "      <td>2556.84000</td>\n",
       "      <td>717.38370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>min</td>\n",
       "      <td>15096.327</td>\n",
       "      <td>14229.664</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>142.29665</td>\n",
       "      <td>2434.00000</td>\n",
       "      <td>150.96327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   metric  sum_player_rating  sum_player_worth  average_player_age   team_id  \\\n",
       "0   count             50.000            50.000           50.000000  50.00000   \n",
       "1     max         719186.300        792043.250           27.000000  50.00000   \n",
       "2  stddev         118708.750        128564.730            0.522913  14.57738   \n",
       "3    mean          71738.375         73451.310           25.568400  25.50000   \n",
       "4     min          15096.327         14229.664           24.340000   1.00000   \n",
       "\n",
       "   average_player_worth  sum_player_age  average_player_rating  \n",
       "0              50.00000        50.00000               50.00000  \n",
       "1            7920.43260      2700.00000             7191.86330  \n",
       "2            1285.64720        52.29132             1187.08750  \n",
       "3             734.51310      2556.84000              717.38370  \n",
       "4             142.29665      2434.00000              150.96327  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc_stats_df = featurestore.visualize_featuregroup_descriptive_stats(\"players_features\")\n",
    "desc_stats_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Datasets\n",
    "\n",
    "To group data in the feature store we use three concepts:\n",
    "\n",
    "- Feature\n",
    "- Feature group\n",
    "- Training Dataset\n",
    "\n",
    "Typically during the feature engineering phase of a machine learning project, you compute a set of features for each type of data that you have, these features are naturally grouped into a documented and versioned **feature group**. \n",
    "\n",
    "In practice, it is common that organizations have many different type of datasets that they can extract features from, for example if you are building a recommendation system you might have demographic data about each user as well as user-activity data. \n",
    "\n",
    "When you train a machine learning model, you want to use all features that have predictive power and that the model can learn from. At this point, we can create a training dataset of features from several different feature groups and use that for training. That is the purpose of the training dataset abstraction. \n",
    "\n",
    "Of course you can always just save a group of features anywhere inside your project, e.g as a csv, or .tfrecords file. However, by using the feature store you can create **managed** training datasets. Managed training datasets will show up in the feature registry UI and will automatically be versioned, documented and reproducible. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once a training dataset have been created you can find it in the featurestore UI in hopsworks under the tab `Training datasets`, from there you can also edit the metadata if necessary. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Training Dataset Path\n",
    "\n",
    "After a **managed dataset** have been created, it is easy to share it and re-use it for training various models. For example if the dataset have been materialized in tf-records format you can call the method `get_training_dataset_path(training_dataset)` to get the HDFS path and read it directly in your tensorflow code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Projects/demo_featurestore_admin000/demo_featurestore_admin000_Training_Datasets/tour_training_dataset_test_1/tour_training_dataset_test'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_training_dataset_path(\"tour_training_dataset_test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the library will look for the training dataset in the project's featurestore and use version 1, but this can be overriden if required:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Projects/demo_featurestore_admin000/demo_featurestore_admin000_Training_Datasets/tour_training_dataset_test_1/tour_training_dataset_test'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_training_dataset_path(\n",
    "    \"tour_training_dataset_test\", \n",
    "    featurestore=featurestore.project_featurestore(),\n",
    "    training_dataset_version=featurestore.get_latest_training_dataset_version(\"tour_training_dataset_test\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Dataset Visualization\n",
    "\n",
    "Just as for featuregroups, the training dataset statistics can be visualized in the Jupyter notebook in `%%local`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_training_dataset_distributions(\"tour_training_dataset_test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also override default parameters and set plotting configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_training_dataset_distributions(\"tour_training_dataset_test\", \n",
    "                                                  featurestore=featurestore.project_featurestore(), \n",
    "                                                  training_dataset_version=1, \n",
    "                                                  figsize=(12, 9),\n",
    "                                                  color='lightblue', \n",
    "                                                  log=False, \n",
    "                                                  align=\"center\", \n",
    "                                                  plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_training_dataset_correlations(\"tour_training_dataset_test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also override default parameters and set plotting configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_training_dataset_correlations(\"tour_training_dataset_test\", \n",
    "                                                 featurestore=None, \n",
    "                                                 training_dataset_version=1, \n",
    "                                                 figsize=(8,6),\n",
    "                                                 cmap=\"coolwarm\", \n",
    "                                                 annot=True, \n",
    "                                                 fmt=\".2f\", \n",
    "                                                 linewidths=.05, \n",
    "                                                 plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_training_dataset_clusters(\"tour_training_dataset_test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also override default parameters and set plotting configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.visualize_training_dataset_clusters(\"tour_training_dataset_test\", \n",
    "                                featurestore=None, \n",
    "                                training_dataset_version=1, \n",
    "                                figsize=(8,6),\n",
    "                                plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Descriptive Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>average_attendance</th>\n",
       "      <th>team_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>count</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>50.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>max</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>92301.0860</td>\n",
       "      <td>21319.5330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stddev</td>\n",
       "      <td>0.522913</td>\n",
       "      <td>14232.3090</td>\n",
       "      <td>5238.9430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mean</td>\n",
       "      <td>25.568400</td>\n",
       "      <td>8669.3940</td>\n",
       "      <td>8723.2920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>min</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>1038.5237</td>\n",
       "      <td>760.8729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   metric  average_player_age  average_attendance  team_budget\n",
       "0   count           50.000000             50.0000      50.0000\n",
       "1     max           27.000000          92301.0860   21319.5330\n",
       "2  stddev            0.522913          14232.3090    5238.9430\n",
       "3    mean           25.568400           8669.3940    8723.2920\n",
       "4     min           24.340000           1038.5237     760.8729"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc_stats_df = featurestore.visualize_training_dataset_descriptive_stats(\"tour_training_dataset_test\")\n",
    "desc_stats_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since descriptive stats is just a pandas table and not a matplotlib figure it does not need a DISPLAY to work, so you can run it from the spark driver or executor as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>average_attendance</th>\n",
       "      <th>team_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>count</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>50.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>max</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>92301.0860</td>\n",
       "      <td>21319.5330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>stddev</td>\n",
       "      <td>0.522913</td>\n",
       "      <td>14232.3090</td>\n",
       "      <td>5238.9430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mean</td>\n",
       "      <td>25.568400</td>\n",
       "      <td>8669.3940</td>\n",
       "      <td>8723.2920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>min</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>1038.5237</td>\n",
       "      <td>760.8729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   metric  average_player_age  average_attendance  team_budget\n",
       "0   count           50.000000             50.0000      50.0000\n",
       "1     max           27.000000          92301.0860   21319.5330\n",
       "2  stddev            0.522913          14232.3090    5238.9430\n",
       "3    mean           25.568400           8669.3940    8723.2920\n",
       "4     min           24.340000           1038.5237     760.8729"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc_stats_df = featurestore.visualize_training_dataset_descriptive_stats(\"tour_training_dataset_test\")\n",
    "desc_stats_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Featurestore Metadata\n",
    "To explore the contents of the featurestore we recommend using the featurestore page in the Hopsworks UI but you can also get the metadata programmatically from the REST API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update Metadata Cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<hops.featurestore_impl.dao.common.featurestore_metadata.FeaturestoreMetadata at 0x7f6e0453bcc0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_featurestore_metadata(update_cache=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Feature Stores Accessible In the Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['demo_featurestore_admin000_featurestore']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_project_featurestores()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Feature Groups in a Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['games_features_1',\n",
       " 'games_features_on_demand_tour_1',\n",
       " 'games_features_hudi_tour_1',\n",
       " 'season_scores_features_1',\n",
       " 'attendances_features_1',\n",
       " 'players_features_1',\n",
       " 'teams_features_1',\n",
       " 'imported_feature_name_1',\n",
       " 'imported_feature_name_online_1']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_featuregroups()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `get_featuregroups()` will use the project's feature store, but this can also be specified with the optional argument featurestore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['games_features_1',\n",
       " 'games_features_on_demand_tour_1',\n",
       " 'games_features_hudi_tour_1',\n",
       " 'season_scores_features_1',\n",
       " 'attendances_features_1',\n",
       " 'players_features_1',\n",
       " 'teams_features_1',\n",
       " 'imported_feature_name_1',\n",
       " 'imported_feature_name_online_1']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_featuregroups(featurestore=featurestore.project_featurestore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Features in a Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['away_team_id',\n",
       " 'home_team_id',\n",
       " 'score',\n",
       " 'away_team_id',\n",
       " 'home_team_id',\n",
       " '_hoodie_commit_seqno',\n",
       " '_hoodie_commit_time',\n",
       " '_hoodie_file_name',\n",
       " '_hoodie_partition_path',\n",
       " '_hoodie_record_key',\n",
       " 'score',\n",
       " 'average_position',\n",
       " 'sum_position',\n",
       " 'team_id',\n",
       " 'average_attendance',\n",
       " 'sum_attendance',\n",
       " 'team_id',\n",
       " 'average_player_age',\n",
       " 'average_player_rating',\n",
       " 'average_player_worth',\n",
       " 'sum_player_age',\n",
       " 'sum_player_rating',\n",
       " 'sum_player_worth',\n",
       " 'team_id',\n",
       " 'team_budget',\n",
       " 'team_id',\n",
       " 'team_position',\n",
       " 'away_team_id',\n",
       " 'home_team_id',\n",
       " 'score',\n",
       " 'away_team_id',\n",
       " 'home_team_id',\n",
       " 'score']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_features_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default get_features_list() will use the project's feature store, but this can also be specified with the optional argument featurestore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['away_team_id',\n",
       " 'home_team_id',\n",
       " 'score',\n",
       " 'away_team_id',\n",
       " 'home_team_id',\n",
       " '_hoodie_commit_seqno',\n",
       " '_hoodie_commit_time',\n",
       " '_hoodie_file_name',\n",
       " '_hoodie_partition_path',\n",
       " '_hoodie_record_key',\n",
       " 'score',\n",
       " 'average_position',\n",
       " 'sum_position',\n",
       " 'team_id',\n",
       " 'average_attendance',\n",
       " 'sum_attendance',\n",
       " 'team_id',\n",
       " 'average_player_age',\n",
       " 'average_player_rating',\n",
       " 'average_player_worth',\n",
       " 'sum_player_age',\n",
       " 'sum_player_rating',\n",
       " 'sum_player_worth',\n",
       " 'team_id',\n",
       " 'team_budget',\n",
       " 'team_id',\n",
       " 'team_position',\n",
       " 'away_team_id',\n",
       " 'home_team_id',\n",
       " 'score',\n",
       " 'away_team_id',\n",
       " 'home_team_id',\n",
       " 'score']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_features_list(featurestore=featurestore.project_featurestore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Training Datasets in a Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tour_training_dataset_test_1',\n",
       " 'games_features_all_1',\n",
       " 'team_position_prediction_1',\n",
       " 'games_features_all_2',\n",
       " 'team_position_prediction_2',\n",
       " 'games_features_all_3',\n",
       " 'team_position_prediction_3',\n",
       " 'games_features_all_4']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_training_datasets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `get_training_datasets()` will use the project's feature store, but this can also be specified with the optional argument featurestore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tour_training_dataset_test_1',\n",
       " 'games_features_all_1',\n",
       " 'team_position_prediction_1',\n",
       " 'games_features_all_2',\n",
       " 'team_position_prediction_2',\n",
       " 'games_features_all_3',\n",
       " 'team_position_prediction_3',\n",
       " 'games_features_all_4',\n",
       " 'team_position_prediction_4']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_training_datasets(featurestore=featurestore.project_featurestore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Storage Connectors in a Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('demo_featurestore_admin000_meb1_onlinefeaturestore', 'JDBC'),\n",
       " ('demo_featurestore_admin000', 'JDBC'),\n",
       " ('demo_featurestore_admin000_featurestore', 'JDBC'),\n",
       " ('s3_connector', 'S3'),\n",
       " ('demo_featurestore_admin000_Training_Datasets', 'HOPSFS')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_storage_connectors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `get_storage_connectors()` will use the project's feature store, but this can also be specified with the optional argument featurestore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('demo_featurestore_admin000_meb1_onlinefeaturestore', 'JDBC'),\n",
       " ('demo_featurestore_admin000', 'JDBC'),\n",
       " ('demo_featurestore_admin000_featurestore', 'JDBC'),\n",
       " ('s3_connector', 'S3'),\n",
       " ('demo_featurestore_admin000_Training_Datasets', 'HOPSFS')]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_storage_connectors(featurestore=featurestore.project_featurestore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get All Metadata (Features, Feature groups, Training Datasets) for a Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<hops.featurestore_impl.dao.common.featurestore_metadata.FeaturestoreMetadata at 0x7f6e03c3ea58>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_featurestore_metadata()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `get_featurestore_metadata` will use the project's feature store, but this can also be specified with the optional argument featurestore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<hops.featurestore_impl.dao.common.featurestore_metadata.FeaturestoreMetadata at 0x7f6e03c3ea58>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featurestore.get_featurestore_metadata(featurestore=featurestore.project_featurestore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attach Metadata to a Feature Group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature store enables users to attach a dictionary of metadata to a feature group. It is only supported for CachedFeatureGroups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.add_metadata(\"teams_features\", {\"attr1\" : \"attr1 value\", \"attr2\" : \"attr2 value\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get All Metadata associated to a Feature Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.get_metadata(\"teams_features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the metadata by their keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.get_metadata(\"teams_features\", [\"attr1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove metadata attached to a Feature Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.remove_metadata(\"teams_features\", [\"attr1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Feature group from S3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you already have feature engineered data on S3 you can import it in the Hopsworks Featurestore using the following method.\n",
    "\n",
    "Before running it, you should make sure that the `storage_connector` has been created in Hopsworks. \n",
    "Furthermore, you can define the primary key of the feature group, which can be a composite key of multiple columns. Please make sure the primary key is unique. The dataset we are importing as a feature group has a composite primary key `home_team_id` and `away_team_id`. Specifying only one of the two will lead to a non-unique key and lets the import job fail.\n",
    "\n",
    "After invoking the method, you can check the status of the import job from the Jobs UI in your Hopsworks project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.import_featuregroup_s3(storage_connector='s3_connector',\n",
    "                                    featuregroup='imported_feature_name',\n",
    "                                    path='games_features_composite_key_1',\n",
    "                                    description='Feature group imported from S3',\n",
    "                                    primary_key=['home_team_id', 'away_team_id'],\n",
    "                                    data_format='orc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the method will import the data in the offline featurestore. By setting the option `online=True` you can import your features in the online featurestore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.import_featuregroup_s3(storage_connector='s3_connector',\n",
    "                                    featuregroup='imported_feature_name_online',\n",
    "                                    path='games_features_composite_key_1',\n",
    "                                    description='Feature group imported from S3',\n",
    "                                    primary_key=['home_team_id', 'away_team_id'],\n",
    "                                    data_format='orc',\n",
    "                                    online=True,\n",
    "                                    offline=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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